OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the triple structure recommended for annotations in these works (which is isomorphic to the triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which  Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO;  Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually performed by humans or else by machine learning systems. Both of them leave much to be desired: the former, with respect to their annotation rate; the latter, with respect to their (average) precision and recall. In this work, we showed how linguistic tools can be wrapped in order to annotate automatically Semantic Web pages using ontologies. This entails their fast, robust and accurate semantic annotation. As a way of example, as mentioned in Sub-goal 5.5, we developed a particular OntoTagger module for the recognition, classification and labelling of named entities, according to the MUC and ACE tagsets (Chinchor, 1997; Doddington et al., 2004). These tagsets were further specified by means of a domain ontology, namely the Cinema Named Entities Ontology (CNEO). This module was applied to the automatic annotation of ten different web pages containing cinema reviews (that is, around 5000 words). In addition, the named entities annotated with this module were also labelled as instances (or individuals) of the classes included in the CNEO and, then, were used to populate this domain ontology. • The statistical results obtained from the evaluation of this particular module of OntoTagger can be summarised as follows. On the one hand, as far as recall (R) is concerned, (R.1) the lowest value was 76,40% (for file 7); (R.2) the highest value was 97, 50% (for file 3); and (R.3) the average value was 88,73%. On the other hand, as far as the precision rate (P) is concerned, (P.1) its minimum was 93,75% (for file 4); (R.2) its maximum was 100% (for files 1, 5, 7, 8, 9, and 10); and (R.3) its average value was 98,99%. • These results, which apply to the tasks of named entity annotation and ontology population, are extraordinary good for both of them. They can be explained on the basis of the high accuracy of the annotations provided by OntoTagger at the lower levels (mainly at the morphosyntactic level). However, they should be conveniently qualified, since they might be too domain- and/or language-dependent. It should be further experimented how our approach works in a different domain or a different language, such as French, English, or German. • In any case, the results of this application of Human Language Technologies to Ontology Population (and, accordingly, to Ontological Engineering) seem very promising and encouraging in order for these two areas to collaborate and complement each other in the area of semantic annotation. Fifth, as shown in the State of the Art of this work, there are different approaches and models for the semantic annotation of texts, but all of them focus on a particular view of the semantic level. Clearly, all these approaches and models should be integrated in order to bear a coherent and joint semantic annotation level. OntoTag shows how (i) these semantic annotation layers could be integrated together; and (ii) they could be integrated with the annotations associated to other annotation levels. Sixth, we identified some recommendations, best practices and lessons learned for annotation standardisation, interoperation and merge. They show how standardisation (via ontologies, in this case) enables the combination, integration and interoperation of different linguistic tools and their annotations into a multilayered (or multileveled) linguistic annotation, which is one of the hot topics in the area of Linguistic Annotation. And last but not least, OntoTag’s annotation scheme and OntoTagger’s annotation schemas show a way to formalise and annotate coherently and uniformly the different units and features associated to the different levels and layers of linguistic annotation. This is a great scientific step ahead towards the global standardisation of this area, which is the aim of ISO/TC 37 (in particular, Subcommittee 4, dealing with the standardisation of linguistic annotations and resources).

Model of the aerodynamic behavior of a pararotor

Asimple semi-empirical model for the aerodynamic behavior of a low-aspect ratio pararotor in autorotation at low Reynolds numbers is presented. The paper is split into three sections: Sec. II deals with the theoretical model derivation, Sec. III deals with the wind-tunnel measurements needed for tuning the theoretical model, and Sec. IV deals with the tuning between the theoretical model and the experimental data. The study is focused on the effect of both the blade pitch angle and the blade roughness and also on the stream velocity, on the rotation velocity, and on the drag of a model. Flow pattern visualizations have also been performed. The value of the free aerodynamic parameters of the semi-empirical model that produces the best fit with the experimental results agrees with the expected ones for the blades at the test conditions. Finally, the model is able to describe the behavior of a pararotor in autorotation that rotates fixed to a shaft, validated for a range of blade pitch angles. The movement of the device is found to be governed by a reduced set of dimensionless parameters.

Analysis of genomic alterations in benign, atypical, and anaplastic meningiomas: Toward a genetic model of meningioma progression

Nineteen benign [World Health Organization (WHO) grade I; MI], 21 atypical (WHO grade II; MII), and 19 anaplastic (WHO grade III; MIII) sporadic meningiomas were screened for chromosomal imbalances by comparative genomic hybridization (CGH). These data were supplemented by molecular genetic analyses of selected chromosomal regions and genes. With increasing malignancy grade, a marked accumulation of genomic aberrations was observed; i.e., the numbers (mean ± SEM) of total alterations detected per tumor were 2.9 ± 0.7 for MI, 9.2 ± 1.2 for MII, and 13.3 ± 1.9 for MIII. The most frequent alteration detected in MI was loss on 22q (58%). In MII, aberrations most commonly identified were losses on 1p (76%), 22q (71%), 14q (43%), 18q (43%), 10 (38%), and 6q (33%), as well as gains on 20q (48%), 12q (43%), 15q (43%), 1q (33%), 9q (33%), and 17q (33%). In MIII, most of these alterations were found at similar frequencies. However, an increase in losses on 6q (53%), 10 (68%), and 14q (63%) was observed. In addition, 32% of MIII demonstrated loss on 9p. Homozygous deletions in the CDKN2A gene at 9p21 were found in 4 of 16 MIII (25%). Highly amplified DNA sequences were mapped to 12q13–q15 by CGH in 1 MII. Southern blot analysis of this tumor revealed amplification of CDK4 and MDM2. By CGH, DNA sequences from 17q were found to be amplified in 1 MII and 8 MIII, involving 17q23 in all cases. Despite the high frequency of chromosomal aberrations in the MII and MIII investigated, none of these tumors showed mutations in exons 5–8 of the TP53 gene. On the basis of the most common aberrations identified in the various malignancy grades, a model for the genomic alterations associated with meningioma progression is proposed.

Role of a pineal cAMP-operated arylalkylamine N-acetyltransferase/14-3-3-binding switch in melatonin synthesis

The daily rhythm in melatonin levels is controlled by cAMP through actions on the penultimate enzyme in melatonin synthesis, arylalkylamine N-acetyltransferase (AANAT; serotonin N-acetyltransferase, EC 2.3.1.87). Results presented here describe a regulatory/binding sequence in AANAT that encodes a cAMP-operated binding switch through which cAMP-regulated protein kinase-catalyzed phosphorylation [RRHTLPAN → RRHpTLPAN] promotes formation of a complex with 14-3-3 proteins. Formation of this AANAT/14-3-3 complex enhances melatonin production by shielding AANAT from dephosphorylation and/or proteolysis and by decreasing the Km for 5-hydroxytryptamine (serotonin). Similar switches could play a role in cAMP signal transduction in other biological systems.

NMR evidence for the participation of a low-barrier hydrogen bond in the mechanism of delta 5-3-ketosteroid isomerase.

Delta 5-3-Ketosteroid isomerase (EC 5.3.3.1) promotes an allylic rearrangement involving intramolecular proton transfer via a dienolic intermediate. This enzyme enhances the catalytic rate by a factor of 10(10). Two residues, Tyr-14, the general acid that polarizes the steroid 3-carbonyl group and facilitates enolization, and Asp-38 the general base that abstracts and transfers the 4 beta-proton to the 6 beta-position, contribute 10(4.7) and 10(5.6) to the rate increase, respectively. A major mechanistic enigma is the huge disparity between the pKa values of the catalytic groups and their targets. Upon binding of an analog of the dienolate intermediate to isomerase, proton NMR detects a highly deshielded resonance at 18.15 ppm in proximity to aromatic protons, and with a 3-fold preference for protium over deuterium (fractionation factor, phi = 0.34), consistent with formation of a short, strong (low-barrier) hydrogen bond to Tyr-14. The strength of this hydrogen bond is estimated to be at least 7.1 kcal/mol. This bond is relatively inaccessible to bulk solvent and is pH insensitive. Low-barrier hydrogen bonding of Tyr-14 to the intermediate, in conjunction with the previously demonstrated tunneling contribution to the proton transfer by Asp-38, provide a plausible and quantitative explanation for the high catalytic power of this isomerase.

Isolation of cDNA clones encoding RICH: a protein induced during goldfish optic nerve regeneration with homology to mammalian 2',3'-cyclic-nucleotide 3'-phosphodiesterases.

Using data derived from peptide sequencing of p68/70, a protein doublet induced during optic nerve regeneration in goldfish, we have isolated cDNAs that encode RICH (regeneration-induced CNPase homolog) from a goldfish regenerating retina cDNA library. The predicted RICH protein comprises 411 amino acids, possesses a pI of 4.48, and shows significant homology to the mammalian myelin marker enzyme 2',3'-cyclic-nucleotide 3'-phosphodiesterase (CNPase; EC 3.1.4.37). The mRNA encoding RICH was demonstrated, by both Northern blot analysis and RNase protection assays, to be induced as much as 8-fold in regenerating goldfish retinas at 20 days after nerve crush. Analysis of total RNA samples from various tissues showed a broad distribution of RICH mRNA, with the highest levels observed in gravid ovary. The data obtained strongly suggest that RICH is identical or very similar to p68/70. The molecular cloning of RICH provides the means for a more detailed analysis of its function in nerve regeneration. Additionally, the homology of RICH and CNPase suggests that further investigation may provide additional insight into the role of these proteins in the nervous system.

Two helices plus a linker: a small model substrate for eukaryotic RNase P.

Using precursor tRNA molecules to study RNA-protein interactions, we have identified an RNA motif recognized by eukaryotic RNase P (EC 3.1.26.5). Analysis of circularly permuted precursors indicates that interruptions in the sugar-phosphate backbone are not tolerated in the acceptor stem, in the T stem-loop, or between residues A-9 and G-10. Prokaryotic RNase P will function with a minihelix consisting of the acceptor stem connected directly to the T stem-loop. Eukaryotic RNase P cannot use such a minimal substrate unless a linker sequence is added in the gap where the D stem and anticodon stem-loop were deleted.

South America, 1807 (Image 3 of 7) (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: Colombia Prima or South America : in which it has been attempted to delineate the extent of our knowledge of that continent, extracted chiefly from the original manuscript maps of ... Pinto, likewise fom those of João Joaquin da Rocha, João da Costa Ferreira ... Francisco Manuel Sobreviela &c. and from the most authentic edited accounts of those countries, digested & constructed by ... Louis Stanislas D'Arcy Delarochette. It was published by William Faden geographer to His Majesty and to His Royal Highness the Prince of Wales in June 4th, 1807. Scale [ca. 1:3,000,000]. This layer is image 3 of 7 total images of the eight sheet map, representing the eastern portion of the map.The image inside the map neatline is georeferenced to the surface of the earth and fit to the South America Lambert Conformal Conic projected coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as drainage, cities and other human settlements, roads, territorial boundaries, shoreline features, mines, tribes, and more. Relief shown by hachures. Includes notes.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

Jamaica, 1763 (Image 3 of 4) (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: This map of the island of Jamaica; laid down from the papers and under the direction of Henry Moore, Esqr.; His Majesty's Lieutenant Governor and Commander in Chief of that island, in the years 1756, 57, 58, 59, 60 & 61; & from a great number of actual surveys performed by the publishers is humbly inscribed by his lordship's most obedient & most humble servants, Thos. Craskell, engineer, Jas. Simpson, surveyor. It was published by D. Fournier in 1763. Scale [ca 1:200,000]. This layer is image 3 of 4 total images of the four sheet map, representing the northeast portion of the map. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Jamaica Grid projected coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as towns, villages, and other human settlements, roads, drainage, selected buildings, ground cover, shoreline features, and more. Relief shown pictorially. Includes also illustrations.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

County of Cornwall, Jamaica, 1763 (Image 3 of 4) (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: This map of the county of Cornwall, in the island of Jamaica; laid down from the papers, under the direction of, Henry Moore, Esqr., His Majesty's Lieutenant Governor, and Commander in Chief of that island, in the years 1756, 57, 58, 59, 60 & 61, & from a great number of actual surveys performed by the publishers is humbly inscribed by his lordship's most obedient & humble servants, Thos. Craskell, engineer, Jas. Simpson, surveyor. It was published by D. Fournier in 1763. Scale [ca. 1:95,000]. This layer is image 3 of 4 total images of the four sheet source map, representing the northeast portion of the map. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Jamaica Grid projected coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as towns, villages, and other human settlements, roads, parish boundaries, drainage, selected buildings and names of landowners, ground cover, shoreline features, shoals, channels, anchorage points, and more.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

County of Middlesex, Jamaica, 1763 (Image 3 of 4) (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: This map of the county of Middlesex in the island of Jamaica; laid down from the papers, and under the direction of Henry Moore, Esqr., His Majesty's Lieutenant Governor and Commander in Chief of that island, in the years 1756, 57, 58, 60, & 61 and from a great number of actual surveys performed by the publishers is humbly inscribed by his lordship's most obedient & most humble servants, Thos. Craskell, engineer, Jas. Simpson, surveyor. It was published by D. Fournier in 1763. Scale [ca. 1:95,000]. The layer is image 3 of 4 total images of the four sheet map, representing the northwest portion of the map. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Jamaica Grid projected coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as towns, villages, and other human settlements, roads, parish boundaries, drainage, selected buildings and names of landowners, ground cover, shoreline features, shoals, channels, anchorage points, and more. This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

County of Surrey, Jamaica, 1763 (Sheet 3 of 4) (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: This map of the county of Surry in the island of Jamaica; laid down from the papers and under the direction of Henry Moore, Esqr., His Majesty's Lieutenant Governor and Commander in Chief of that island, in the years 1756, 57, 58, 59, 60 & 61, & from a great number of actual surveys performed by the publishers is humbly inscribed, by his most obedient and humble servants, Thos. Craskell, engineer, Jas. Simpson, surveyor. It was published by D. Fournier in 1763. The layer is image 3 of 4 total images of the four sheet map, representing the southwest portion of the map. Scale ca. 1:100,000. Covers County of Surrey, Jamaica. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Jamaica Grid projected coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as roads, drainage, cities and other human settlements, parish boundaries, shoreline features, plantations, and more. Includes also illustrations.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.